Deep Learning for Human Part Discovery in Images

Abstract: This paper addresses the problem of human body part segmentation in
conventional RGB images, which has several applications in robotics,
such as learning from demonstration and human-robot handovers. The
proposed solution is based on Convolutional Neural Networks (CNNs).
We present a network architecture that assigns each pixel to one of
a predefined set of human body part classes, such as head, torso,
arms, legs. After initializing weights with a very deep
convolutional network for image classification, the network can be
trained end-to-end and yields precise class predictions at the
original input resolution. Our architecture particularly improves
on over-fitting issues in the up-convolutional part of the network.
Relying only on RGB rather than RGB-D images also allows us to apply the
approach outdoors. The network achieves state-of-the-art
performance on the PASCAL Parts dataset. Moreover, we introduce two
new part segmentation datasets, the Freiburg sitting people dataset
and the Freiburg people in disaster dataset. We also present
results obtained with a ground robot and an unmanned aerial vehicle.